activity
20182020
most citedTripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots

10 citations · 10 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CL2020

CharBERT: Character-aware Pre-trained Language Model

Wentao Ma, Yiming Cui, Chenglei Si +3

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almos…

cs.CL2020

Conversational Word Embedding for Retrieval-Based Dialog System

Wentao Ma, Yiming Cui, Ting Liu +3

Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR…

cs.CL201910 cited

TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots

Wentao Ma, Yiming Cui, Nan Shao +5

We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to…

cs.CL2018

Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

Zhipeng Chen, Yiming Cui, Wentao Ma +2

Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper,…

cs.CL2018

A Span-Extraction Dataset for Chinese Machine Reading Comprehension

Yiming Cui, Ting Liu, Wanxiang Che +5

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in…

cs.CL2018

HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension

Zhipeng Chen, Yiming Cui, Wentao Ma +3

This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neura…